Record 19032026 · captured 2026-08-25
The world looked up Joe Kent. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
Joseph Clay Kent is an American politician, former United States Army warrant officer, and former Central Intelligence Agency paramilitary officer who served as the director of the National Counterterrorism Center from 2025 to 2026. A member of the Republican
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Cesario Estrada "Cesar" Chavez was an American labor unionist and political activist. Along with Dolores Huerta and Gilbert Padilla, he co-founded the National Farm Workers Association (NFWA), which later merged with the Agricultural Workers Organizing Committ
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
The World Baseball Classic (WBC), also referred to as The Classic, is a quadrennial international baseball tournament sanctioned by the World Baseball Softball Confederation (WBSC), the sport's global governing body, and organized by World Baseball Classic Inc
Ali Ardashir Larijani was an Iranian politician, military officer, and philosopher who served as the secretary of the Supreme National Security Council from 2025 until his assassination in 2026. He had previously served in the position from 2005 to 2007. From
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Michael Bakari Jordan is an American actor, producer, and director. His accolades include an Academy Award, three Actor Awards, and a Producers Guild Award, in addition to nominations for a British Academy Film Award, a Golden Globe Award and two Emmy Awards.
Shannon Mary Kent was a United States Navy cryptologic technician and member of JSOC's Intelligence Support Activity who was killed in the 2019 Manbij bombing. She was the wife of Joe Kent, who entered politics in response to her death.
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
Benjamin Netanyahu, nicknamed "Bibi", is an Israeli politician and diplomat who has served as Prime Minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Dune: Part Three is an upcoming American epic space opera film co-produced and directed by Denis Villeneuve, who co-wrote the screenplay with Brian K. Vaughan. Based on the 1969 novel Dune Messiah by Frank Herbert, it is the sequel to Dune: Part Two (2024) and
The 2025–26 UEFA Champions League was the 71st season of Europe's premier club football tournament organised by UEFA, and the 34th season since it was rebranded from the European Cup to the UEFA Champions League.
Dolores Huerta is an American labor leader and feminist activist. After working for several years with the Community Service Organization (CSO), she co-founded the National Farm Workers Association (NFWA) with fellow activists Cesar Chavez and Gilbert Padilla,
The 98th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 15, 2026, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 24 categories honoring
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
Markwayne Mullin is an American politician and businessman who has served since 2026 as the ninth United States secretary of homeland security. A member of the Republican Party, Mullin served from 2023 to 2026 as the junior United States senator from Oklahoma
The 2026 World Baseball Classic was an international professional baseball tournament between 20 national baseball teams, and the sixth iteration of the World Baseball Classic (WBC). It ran from March 5 to 17, 2026. The pool-play rounds were played in LoanDepo
Tulsi Gabbard is an American politician and U.S. Army officer who served as the eighth Director of National Intelligence (DNI) from 2025 to 2026. She previously served as U.S. representative for Hawaii's 2nd congressional district from 2013 to 2021 and in the
Banksy is a pseudonymous England-based street artist, political activist, and film director. He has never publicly confirmed his identity. Active since the 1990s, his satirical street art and subversive epigrams combine dark humour with graffiti executed in a
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
Jessie Buckley is an Irish actress and singer. Her accolades include an Academy Award, two BAFTAs, an Actor Award, a Golden Globe Award, a Critics' Choice Award and a Laurence Olivier Award.
The 2025 Africa Cup of Nations was the 35th edition of the Africa Cup of Nations, a biennial football tournament contested by the men's national teams of the Confederation of African Football (CAF). It was hosted by Morocco — the second time the country had ho
Saint Patrick's Day, or the Feast of Saint Patrick, is a religious and cultural holiday held on 17 March, the traditional death date of Saint Patrick, the foremost patron saint of Ireland.
The Africa Cup of Nations, commonly abbreviated as AFCON in English and CAN in French, and currently known as the TotalEnergies Africa Cup of Nations for sponsorship reasons, is the main biennial international men's association football competition in Africa.
Aaron Gagnon is a Canadian former professional ice hockey centre who played in the National Hockey League (NHL) with the Dallas Stars and Winnipeg Jets.
p16, is a protein that slows cell division by slowing the progression of the cell cycle from the G1 phase to the S phase, thereby acting as a tumor suppressor. It is encoded by the CDKN2A gene. A deletion in this gene can result in insufficient or non-function
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors. Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate
Theoretical Foundations of δ-margin Majority Voting
In high-stakes ML applications such as fraud detection, medical diagnostics, and content moderation, practitioners rely on consensus-based approaches to control prediction quality. A particularly valuable technique -- δδδ-margin majority voting -- collects votes sequentially until one label exceeds alternatives by a threshold δδδ, offering stronger confidence than simple majority voting. Despite widespread adoption,
The Moral Foundations Reddit Corpus
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for mora
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Federated Learning (FL) is a popular approach for distributed deep learning that prevents the pooling of large amounts of data in a central server. FL relies on clients to update a global model using their local datasets. Classical FL algorithms use a central federator that, for each training round, waits for all clients to send their model updates before aggregating them. In practical deployments, clients might have
The MCC approaches the geometric mean of precision and recall as true negatives approach infinity
The performance of a binary classifier is described by a confusion matrix with four entries: the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The Matthews Correlation Coefficient (MCC), F1, and Fowlkes-Mallows (FM) scores are scalars that summarize a confusion matrix. Both the F1 and FM scores are based on only three of the four entries in a confusion matrix (the
Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a
Bridge Diffusion Model: Bridge Chinese Text-to-Image Diffusion Model with English Communities
Text-to-Image generation (TTI) technologies are advancing rapidly, especially in the English language communities. However, apart from the user input language barrier problem, English-native TTI models inherently carry biases from their English world centric training data, which creates a dilemma for development of other language-native TTI models. One common choice is to fine-tune the English-native TTI model with t
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning these pretrained models on downstream datasets provides further significant performance gains; however, this process typically requires a large number of expensive, high-end GPUs. Although there have been efforts focused on parameter-efficient fine-tuning, they cannot fully unlock th
Feature Space Renormalization for Semi-supervised Learning
Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabeled data to alleviate models'dependence on large labeled datasets. The common framework among recent approaches is to train the model on a large amount of unlabeled data with consistency regularization to constrain the model predictions to be invariant to input perturbation. This paper proposes a feature space renormalizat
MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models
We introduce MultiMedEval, an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM). MultiMedEval comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. The chosen tasks and performance metrics are based on their widespread adoption in the community and their div
CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics
Modern human sensing applications often rely on data distributed across users and devices, where privacy concerns prevent centralized training. Federated Learning (FL) addresses this challenge by enabling collaborative model training without exposing raw data or attributes. However, achieving fairness in such settings remains difficult, as most human sensing datasets lack demographic labels, and FL's privacy guar
Hi-GMAE: Hierarchical Graph Masked Autoencoders
Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning approach for graph-structured data. Existing GMAE models primarily focus on reconstructing node-level information, categorizing them as single-scale GMAEs. This methodology, while effective in certain contexts, tends to overlook the complex hierarchical structures inherent in many real-world graphs. For instance, molecular graphs exh
Demystifying amortized causal discovery with transformers
Supervised learning for causal discovery from observational data often achieves competitive performance despite seemingly avoiding the explicit assumptions that traditional methods require for identifiability. In this work, we analyze CSIvA (Ke et al., 2023) on bivariate causal models, a transformer architecture for amortized inference promising to train on synthetic data and transfer to real ones. First, we bridge t
UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation
Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networks may risk failure post-deployment due to environmental distribution shifts at test time. In light
Urban morphology is fundamental to determining urban functionality and vitality. Prevailing simulation methods, however, often oversimplify morphological generation as a geometric problem, lacking a profound understanding of urban semantics and geographical context. To address this limitation, this study proposes ControlCity, a diffusion model that achieves comprehensive urban morphology generation through multimodal
Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction
Trajectory prediction in autonomous driving has traditionally been studied from a model-centric perspective. However, existing datasets exhibit a strong long-tail distribution in scenario density, where common low-density cases dominate and safety-critical high-density cases are severely underrepresented. This imbalance limits model robustness and hides failure modes when standard evaluations average errors across al
Efficient Diffusion as Low Light Enhancer
The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency. In this paper, we identify two primary factors contributing to performance degradation: fitting
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing reinforcement learning based methods for circuit design los
Fine-Grained Uncertainty Quantification via Collisions
We propose a new and intuitive metric for aleatoric uncertainty quantification (UQ), the prevalence of class collisions defined as the same input being observed in different classes. We use the rate of class collisions to define the collision matrix, a novel and uniquely fine-grained measure of uncertainty. For a classification problem involving $K$ classes, the $K\times K$ collision matrix $S$ measures the inherent
Conditional Distribution Learning for Graph Classification
Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to increase the dissimilarity between negative pairs of node embeddings. This inevitably results in a confli
Deep learning models for pulmonary disease screening from Computed Tomography (CT) scans promise to alleviate the immense workload on radiologists. Still, their high computational cost, stemming from processing entire 3D volumes, remains a major barrier to widespread clinical adoption. Current sub-sampling techniques often compromise diagnostic integrity by introducing artifacts or discarding critical information. To
Mamba2D: A Natively Multi-Dimensional State-Space Model for Vision Tasks
State-Space Models (SSMs) have emerged as an efficient alternative to transformers, yet existing visual SSMs retain deeply ingrained biases from their origins in natural language processing. In this paper, we address these limitations by introducing M2D-SSM, a ground-up re-derivation of selective state-space techniques for multidimensional data. Unlike prior works that apply 1D SSMs directly to images through arbitra
Quantum-enhanced causal discovery for a small number of samples
The discovery of causal relations from observed data has attracted significant interest from disciplines such as economics, social sciences, and biology. In practical applications, considerable knowledge of the underlying systems is often unavailable, and real data are usually associated with nonlinear causal structures, which makes the direct use of most conventional causality analysis methods difficult. This study
Multilingual LLMs Struggle to Link Orthography and Semantics in Bilingual Word Processing
Bilingual lexical processing is shaped by the complex interplay of phonological, orthographic, and semantic features of two languages within an integrated mental lexicon. In humans, this is evident in the ease with which cognate words - words similar in both orthographic form and meaning (e.g., blind, meaning "sightless" in both English and German) - are processed, compared to the challenges posed by interlin
Oracular Programming: A Modular Foundation for Building LLM-Enabled Software
Large Language Models (LLMs) can solve previously intractable tasks given only natural-language instructions and a few examples, but they remain difficult to steer precisely and lack a key capability for building reliable software at scale: the modular composition of computations under enforceable contracts. As a result, they are often embedded in larger software pipelines that use domain-specific knowledge to decomp
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